一、【深度学习图像识别课程】tensorflow迁移学习系列:VGG16花朵分类
转自:https://blog.csdn.net/weixin_41770169/article/details/80330581
花朵数据库介绍
种类5种:daisy雏菊,dandelion蒲公英,rose玫瑰,sunflower向日葵,tulips郁金香
数量: 633, 898, 641, 699, 799
总数量:3670
实战:VGGNet实现花朵分类
1、读入VGG16模型
- from urllib.request import urlretrieve
- from os.path import isfile, isdir
- from tqdm import tqdm
- vgg_dir = 'tensorflow_vgg/'
- # Make sure vgg exists
- if not isdir(vgg_dir):
- raise Exception("VGG directory doesn't exist!")
- class DLProgress(tqdm):
- last_block = 0
- def hook(self, block_num=1, block_size=1, total_size=None):
- self.total = total_size
- self.update((block_num - self.last_block) * block_size)
- self.last_block = block_num
- if not isfile(vgg_dir + "vgg16.npy"):
- with DLProgress(unit='B', unit_scale=True, miniters=1, desc='VGG16 Parameters') as pbar:
- urlretrieve(
- 'https://s3.amazonaws.com/content.udacity-data.com/nd101/vgg16.npy',
- vgg_dir + 'vgg16.npy',
- pbar.hook)
- else:
- print("Parameter file already exists!")
下载了如下标亮文件:vgg16.npy
2、读入图像库
- import tarfile
- dataset_folder_path = 'flower_photos'
- class DLProgress(tqdm):
- last_block = 0
- def hook(self, block_num=1, block_size=1, total_size=None):
- self.total = total_size
- self.update((block_num - self.last_block) * block_size)
- self.last_block = block_num
- if not isfile('flower_photos.tar.gz'):
- with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Flowers Dataset') as pbar:
- urlretrieve(
- 'http://download.tensorflow.org/example_images/flower_photos.tgz',
- 'flower_photos.tar.gz',
- pbar.hook)
- if not isdir(dataset_folder_path):
- with tarfile.open('flower_photos.tar.gz') as tar:
- tar.extractall()
- tar.close()
下载如下高亮文件:flower_photos.tar.gz
3、卷积代码
参考的源码:[html] view plain cop
- self.conv1_1 = self.conv_layer(bgr, "conv1_1")
- self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
- self.pool1 = self.max_pool(self.conv1_2, 'pool1')
- self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
- self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
- self.pool2 = self.max_pool(self.conv2_2, 'pool2')
- self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
- self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
- self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
- self.pool3 = self.max_pool(self.conv3_3, 'pool3')
- self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
- self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
- self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
- self.pool4 = self.max_pool(self.conv4_3, 'pool4')
- self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
- self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
- self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
- self.pool5 = self.max_pool(self.conv5_3, 'pool5')
- self.fc6 = self.fc_layer(self.pool5, "fc6")
- self.relu6 = tf.nn.relu(self.fc6)
- with tf.Session() as sess:
- vgg = vgg16.Vgg16()
- input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
- with tf.name_scope("content_vgg"):
- vgg.build(input_)
- feed_dict = {input_: images}
- codes = sess.run(vgg.relu6, feed_dict=feed_dict)
tensorflow中vgg_16采用的上述结构。本项目代码如下:
- import os
- import numpy as np
- import tensorflow as tf
- from tensorflow_vgg import vgg16
- from tensorflow_vgg import utils
- data_dir = 'flower_photos/'
- contents = os.listdir(data_dir)
- classes = [each for each in contents if os.path.isdir(data_dir + each)]
将图像批量batches通过VGG模型,将输出作为新的输入:
- # Set the batch size higher if you can fit in in your GPU memory
- batch_size = 10
- codes_list = []
- labels = []
- batch = []
- codes = None
- with tf.Session() as sess:
- vgg = vgg16.Vgg16()
- input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
- with tf.name_scope("content_vgg"):
- vgg.build(input_)
- for each in classes:
- print("Starting {} images".format(each))
- class_path = data_dir + each
- files = os.listdir(class_path)
- for ii, file in enumerate(files, 1):
- # Add images to the current batch
- # utils.load_image crops the input images for us, from the center
- img = utils.load_image(os.path.join(class_path, file))
- batch.append(img.reshape((1, 224, 224, 3)))
- labels.append(each)
- # Running the batch through the network to get the codes
- if ii % batch_size == 0 or ii == len(files):
- images = np.concatenate(batch)
- feed_dict = {input_: images}
- codes_batch = sess.run(vgg.relu6, feed_dict=feed_dict)
- # Here I'm building an array of the codes
- if codes is None:
- codes = codes_batch
- else:
- codes = np.concatenate((codes, codes_batch))
- # Reset to start building the next batch
- batch = []
- print('{} images processed'.format(ii))
4、模型建立和测试
图像处理代码和标签:- # read codes and labels from file
- import csv
- with open('labels') as f:
- reader = csv.reader(f, delimiter='\n')
- labels = np.array([each for each in reader if len(each) > 0]).squeeze()
- with open('codes') as f:
- codes = np.fromfile(f, dtype=np.float32)
- codes = codes.reshape((len(labels), -1))
4.1 图像预处理
- from sklearn.preprocessing import LabelBinarizer
- lb = LabelBinarizer()
- lb.fit(labels)
- labels_vecs = lb.transform(labels)
对标签进行one-hot编码:daisy雏菊 dandelion蒲公英 rose玫瑰 sunflower向日葵 tulips郁金香
daisy雏菊 1 0 0 0 0
dandelion蒲公英 0 1 0 0 0
rose玫瑰 0 0 1 0 0
sunflower向日葵 0 0 0 1 0
tulips郁金香 0 0 0 0 1
随机拆分数据集(之前那种直接把集中的部分图像拿出来验证/测试不管用,这里的数据集是每个种类集中放的,如果直接拿出其中的一部分,会导致验证集或者测试集是同一种花)。scikit-learn中的函数StratifiedShuffleSplit可以做到。我们这里,随机拿出20%的图像用来验证和测试,然后验证集和测试集再各占一半。
- from sklearn.model_selection import StratifiedShuffleSplit
- ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)
- train_idx, val_idx = next(ss.split(codes, labels))
- half_val_len = int(len(val_idx)/2)
- val_idx, test_idx = val_idx[:half_val_len], val_idx[half_val_len:]
- train_x, train_y = codes[train_idx], labels_vecs[train_idx]
- val_x, val_y = codes[val_idx], labels_vecs[val_idx]
- test_x, test_y = codes[test_idx], labels_vecs[test_idx]
- print("Train shapes (x, y):", train_x.shape, train_y.shape)
- print("Validation shapes (x, y):", val_x.shape, val_y.shape)
- print("Test shapes (x, y):", test_x.shape, test_y.shape)
总数量:3670,则训练图像:3670*0.8=2936,验证图像:3670*0.2*0.5=367,测试图像:3670*0.2*0.5=367。
4.2 层
在上述vgg的基础上,增加一个256个元素的全连接层,最后加上一个softmax层,计算交叉熵进行最后的分类。
- inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])
- labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])
- fc = tf.contrib.layers.fully_connected(inputs_, 256)
- logits = tf.contrib.layers.fully_connected(fc, labels_vecs.shape[1], activation_fn=None)
- cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=labels_, logits=logits)
- cost = tf.reduce_mean(cross_entropy)
- optimizer = tf.train.AdamOptimizer().minimize(cost)
- predicted = tf.nn.softmax(logits)
- correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))
- accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
4.3 训练:batches和epoches
- def get_batches(x, y, n_batches=10):
- """ Return a generator that yields batches from arrays x and y. """
- batch_size = len(x)//n_batches
- for ii in range(0, n_batches*batch_size, batch_size):
- # If we're not on the last batch, grab data with size batch_size
- if ii != (n_batches-1)*batch_size:
- X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size]
- # On the last batch, grab the rest of the data
- else:
- X, Y = x[ii:], y[ii:]
- # I love generators
- yield X, Y
- epochs = 10
- iteration = 0
- saver = tf.train.Saver()
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- for e in range(epochs):
- for x, y in get_batches(train_x, train_y):
- feed = {inputs_: x,
- labels_: y}
- loss, _ = sess.run([cost, optimizer], feed_dict=feed)
- print("Epoch: {}/{}".format(e+1, epochs),
- "Iteration: {}".format(iteration),
- "Training loss: {:.5f}".format(loss))
- iteration += 1
- if iteration % 5 == 0:
- feed = {inputs_: val_x,
- labels_: val_y}
- val_acc = sess.run(accuracy, feed_dict=feed)
- print("Epoch: {}/{}".format(e, epochs),
- "Iteration: {}".format(iteration),
- "Validation Acc: {:.4f}".format(val_acc))
- saver.save(sess, "checkpoints/flowers.ckpt")
验证集的正确率达到90%,很高了已经。
4.4 测试
- with tf.Session() as sess:
- saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
- feed = {inputs_: test_x,
- labels_: test_y}
- test_acc = sess.run(accuracy, feed_dict=feed)
- print("Test accuracy: {:.4f}".format(test_acc))
- %matplotlib inline
- import matplotlib.pyplot as plt
- from scipy.ndimage import imread
- test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'
- test_img = imread(test_img_path)
- plt.imshow(test_img)
- with tf.Session() as sess:
- input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
- vgg = vgg16.Vgg16()
- vgg.build(input_)
- with tf.Session() as sess:
- img = utils.load_image(test_img_path)
- img = img.reshape((1, 224, 224, 3))
- feed_dict = {input_: img}
- code = sess.run(vgg.relu6, feed_dict=feed_dict)
- saver = tf.train.Saver()
- with tf.Session() as sess:
- saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
- feed = {inputs_: code}
- prediction = sess.run(predicted, feed_dict=feed).squeeze()
- plt.imshow(test_img)
- plt.barh(np.arange(5), prediction)
- _ = plt.yticks(np.arange(5), lb.classes_)
上图的花最有可能是Rose,有小概率是Tulips。